121 research outputs found
Automated data-driven creation of the Digital Twin of a brownfield plant
The success of the reconfiguration of existing manufacturing systems, so
called brownfield systems, heavily relies on the knowledge about the system.
Reconfiguration can be planned, supported and simplified with the Digital Twin
of the system providing this knowledge. However, digital models as the basis of
a Digital Twin are usually missing for these plants. This article presents a
data-driven approach to gain knowledge about a brownfield system to create the
digital models of a Digital Twin and their relations. Finally, a proof of
concept shows that process data and position data as data sources deliver the
relations between the models of the Digital Twin.Comment: 7 Pages, 5 figures. Accepted at IEEE ETFA 202
Towards autonomous system: flexible modular production system enhanced with large language model agents
In this paper, we present a novel framework that combines large language
models (LLMs), digital twins and industrial automation system to enable
intelligent planning and control of production processes. Our approach involves
developing a digital twin system that contains descriptive information about
the production and retrofitting the automation system to offer unified
interfaces of fine-granular functionalities or skills executable by automation
components or modules. Subsequently, LLM-Agents are designed to interpret
descriptive information in the digital twins and control the physical system
through RESTful interfaces. These LLM-Agents serve as intelligent agents within
an automation system, enabling autonomous planning and control of flexible
production. Given a task instruction as input, the LLM-agents orchestrate a
sequence of atomic functionalities and skills to accomplish the task. We
demonstrate how our implemented prototype can handle un-predefined tasks, plan
a production process, and execute the operations. This research highlights the
potential of integrating LLMs into industrial automation systems for more
agile, flexible, and adaptive production processes, while also underscoring the
critical insights and limitations for future work
A Knowledge Graph-Based Method for Automating Systematic Literature Reviews
Systematic Literature Reviews aim at investigating current approaches to
conclude a research gap or determine a futuristic approach. They represent a
significant part of a research activity, from which new concepts stem. However,
with the massive availability of publications at a rapid growing rate,
especially digitally, it becomes challenging to efficiently screen and assess
relevant publications. Another challenge is the continuous assessment of
related work over a long period of time and the consequent need for a
continuous update, which can be a time-consuming task. Knowledge graphs model
entities in a connected manner and enable new insights using different
reasoning and analysis methods. The objective of this work is to present an
approach to partially automate the conduction of a Systematic Literature Review
as well as classify and visualize the results as a knowledge graph. The
designed software prototype was used for the conduction of a review on
context-awareness in automation systems with considerably accurate results
compared to a manual conduction.Comment: 9 pages, 7 figures, 2 table
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